!pip install --upgrade eazyml-counterfactual
!pip install gdown python-dotenv

import os
import pandas as pd
import eazyml as ez
from eazyml_counterfactual import (
        ez_cf_inference,
        ez_init        
)
import gdown

from dotenv import load_dotenv
load_dotenv()

True

ez_init(os.getenv('EAZYML_ACCESS_KEY'))

{'success': True,
 'message': 'Initialized successfully. You may revoke your consent to sharing usage stats anytime. You have exclusive paid access.'}

gdown.download_folder(id='1WvIOaIvS7hTlYSkeojYhnBpAWd8HWzDt')

# Defining file paths for training and test datasets and specifying the outcome variable
train_file = os.path.join('data', "airline_train_data.csv")
test_file = os.path.join('data', "airline_test_data.csv")
outcome = "satisfaction"

# Loading the training dataset and the test dataset
train_df = pd.read_csv(train_file)
test_df = pd.read_csv(test_file)

# Display the first few rows of the training DataFrame for inspection
ez.ez_display_df(train_df)

# Clean and preprocess training data
train_df.drop(columns=['id'], inplace=True)  # Remove unnecessary columns
train_df.dropna(inplace=True)  # Remove missing values
train_df.reset_index(drop=True, inplace=True)  # Reset index


# Define model parameters
model_options = {
    "model_type": "predictive",
}

# Build predictive model using EazyML API
build_model_response = ez.ez_build_model(train_df, outcome=outcome, options=model_options)

ez.ez_display_df(build_model_response['global_importance'])

ez.ez_display_df(build_model_response['model_performance'])

# Extract model information from the response dictionary
model_info = build_model_response["model_info"]

# Read test data from a CSV file into a pandas DataFrame
test_data = pd.read_csv(test_file)

# Make predictions using the model, requesting confidence scores and class probabilities
predicted_resp = ez.ez_predict(test_data, model_info, options={"confidence_score": True, "class_probability": True})

# Check if the prediction was successful
if predicted_resp['success']:
    print("Prediction successful")  
    predicted_df = predicted_resp['pred_df']  # Extract the predicted DataFrame
    ez.ez_display_df(predicted_df.head())  # Display the first few rows of the predicted DataFrame
else:
    print("Prediction failed")  
    print(predicted_resp['message'])

Prediction successful

# Define the selected features for prediction
selected_features = ['Gender', 'Customer Type', 'Age', 'Type of Travel', 'Class', 'Flight Distance', 
                     'Inflight wifi service', 'Departure/Arrival time convenient', 'Ease of Online booking', 
                     'Gate location', 'Food and drink', 'Online boarding', 'Seat comfort', 
                     'Inflight entertainment', 'On-board service', 'Leg room service', 
                     'Baggage handling', 'Checkin service', 'Inflight service', 'Cleanliness', 
                     'Departure Delay in Minutes', 'Arrival Delay in Minutes']

# Define variant (modifiable) features
invariants = ['Gender', 'Customer Type', 'Age', 'Type of Travel', 'Class', 'Flight Distance']
variants = [feature for feature in selected_features if feature not in invariants]

# Define configurable parameters for counterfactual inference
cf_options = {   
    "variants": variants,  
    "outcome_ordinality": "SATISFIED",  # Desired outcome 
    "train_data": train_file  
}

# Specify the index of the test record for counterfactual inference
test_index_no = 0  
test_data = predicted_df.loc[[test_index_no]]  

# Perform Inference 
result, optimal_transition_df = ez_cf_inference(
    test_data=test_data,  
    outcome=outcome,  
    selected_features=selected_features,  
    model_info=model_info,  
    options=cf_options  
)

# Summarizes whether an optimal transition was found and the improvement in outcome probability.
ez.ez_display_json(result)

{   'success': True,
    'message': 'Optimal transition found',
    'summary': {   'Actual Outcome': 'NEUTRAL OR DISSATISFIED',
                   'Optimal Outcome': 'SATISFIED',
                   'Improvement in Probability': 0.959}}

# Details the feature changes needed to achieve the optimal outcome.
ez.ez_display_df(optimal_transition_df)

	Variable Name	Importance
0	Ease of Online booking	0.010000
1	Customer Type	0.010000
2	Gate location	0.010000
3	Cleanliness	0.020000
4	On-board service	0.020000
5	Inflight service	0.020000
6	Baggage handling	0.020000
7	Leg room service	0.020000
8	Checkin service	0.020000
9	Seat comfort	0.030000
10	Flight Distance	0.030000
11	Inflight entertainment	0.030000
12	Type of Travel	0.030000
13	Age	0.030000
14	Online boarding	0.150000
15	Class	0.230000
16	Inflight wifi service	0.270000

	Model	Kappa	Accuracy
0	Gradient Boosting Classifier	0.89	0.94
1	Boosted Decision Trees with InformationGain	0.88	0.94
2	Bagged Decision Trees with Information Gain	0.86	0.93
3	Random Forest with Information Gain	0.85	0.93
4	Logistic Regression	0.85	0.93
5	Naive Bayes	0.72	0.87

	id	Gender	Customer Type	Age	Type of Travel	Class	Flight Distance	Inflight wifi service	Departure/Arrival time convenient	Ease of Online booking	Gate location	Food and drink	Online boarding	Seat comfort	Inflight entertainment	On-board service	Leg room service	Baggage handling	Checkin service	Inflight service	Cleanliness	Departure Delay in Minutes	Arrival Delay in Minutes	satisfaction	Probability_satisfaction_NEUTRAL OR DISSATISFIED	Probability_satisfaction_SATISFIED	Predicted satisfaction	Confidence Score
0	23775	FEMALE	DISLOYAL CUSTOMER	20	BUSINESS TRAVEL	ECO	438	4	0	4	3	3	4	3	3	3	3	2	1	3	3	0	0.000000	NEUTRAL OR DISSATISFIED	0.958892	0.041108	NEUTRAL OR DISSATISFIED	95%
1	56344	MALE	LOYAL CUSTOMER	35	BUSINESS TRAVEL	ECO	200	2	2	2	2	2	2	2	2	3	4	1	2	2	2	29	21.000000	NEUTRAL OR DISSATISFIED	0.984734	0.015266	NEUTRAL OR DISSATISFIED	98%
2	23983	FEMALE	LOYAL CUSTOMER	9	PERSONAL TRAVEL	BUSINESS	427	3	3	3	3	1	3	1	1	2	5	3	1	3	1	0	0.000000	NEUTRAL OR DISSATISFIED	0.999934	0.000066	NEUTRAL OR DISSATISFIED	99%
3	119000	MALE	LOYAL CUSTOMER	43	BUSINESS TRAVEL	BUSINESS	479	4	3	4	4	5	5	5	5	3	4	5	5	5	5	272	267.000000	SATISFIED	0.000026	0.999974	SATISFIED	100%
4	75193	FEMALE	LOYAL CUSTOMER	55	BUSINESS TRAVEL	ECO	1744	4	5	5	5	1	4	3	4	4	4	4	2	4	3	7	23.000000	NEUTRAL OR DISSATISFIED	0.960531	0.039469	NEUTRAL OR DISSATISFIED	96%

	Feature	Actual	Optimal	Percentage Change	Absolute Change
0	Gender	Female	Female	N/A	N/A
1	Customer Type	disloyal Customer	disloyal Customer	N/A	N/A
2	Age	20.000000	20.000000	0.000000	0.000000
3	Type of Travel	Business travel	Business travel	N/A	N/A
4	Class	Eco	Eco	N/A	N/A
5	Flight Distance	438.000000	438.000000	0.000000	0.000000
6	Inflight wifi service	4.000000	5.000000	25.000000	1.000000
7	Departure/Arrival time convenient	0.000000	0.000000	N/A	0.000000
8	Ease of Online booking	4.000000	5.000000	25.000000	1.000000
9	Gate location	3.000000	2.000000	-33.300000	-1.000000
10	Food and drink	3.000000	2.000000	-33.300000	-1.000000
11	Online boarding	4.000000	5.000000	25.000000	1.000000
12	Seat comfort	3.000000	4.000000	33.300000	1.000000
13	Inflight entertainment	3.000000	4.000000	33.300000	1.000000
14	On-board service	3.000000	4.000000	33.300000	1.000000
15	Leg room service	3.000000	2.000000	-33.300000	-1.000000
16	Baggage handling	2.000000	1.000000	-50.000000	-1.000000
17	Checkin service	1.000000	1.000000	0.000000	0.000000
18	Inflight service	3.000000	4.000000	33.300000	1.000000
19	Cleanliness	3.000000	4.000000	33.300000	1.000000
20	Departure Delay in Minutes	0.000000	0.000000	N/A	0.000000
21	Arrival Delay in Minutes	0.000000	0.000000	N/A	0.000000

EazyML Counterfactual Template¶

Define Imports¶

1. Initialize EazyML¶

2. Define Dataset Files and Outcome Variable¶

3. Dataset Information¶

Columns in the Dataset:¶

3.1 Display the Dataset¶

4. EazyML Modeling¶

4.1 Building model using the EazyML Modeling API¶

4.2 Feature Importance¶

4.3 Model Importance¶

4.4 Predict Using the Trained EazyML Model¶

5. EazyML Counterfactual Inference¶

5.1 Define Counterfactual Inference Configuration¶

5.2 Perform Counterfactual Inference¶

5.3 Display Results¶

	id	Gender	Customer Type	Age	Type of Travel	Class	Flight Distance	Inflight wifi service	Departure/Arrival time convenient	Ease of Online booking	Gate location	Food and drink	Online boarding	Seat comfort	Inflight entertainment	On-board service	Leg room service	Baggage handling	Checkin service	Inflight service	Cleanliness	Departure Delay in Minutes	Arrival Delay in Minutes	satisfaction
0	124743	Male	Loyal Customer	49	Personal Travel	Eco Plus	399	3	4	3	2	1	3	1	1	5	4	5	5	5	1	0	0.000000	neutral or dissatisfied
1	101297	Male	Loyal Customer	68	Personal Travel	Eco	763	2	4	2	3	3	2	3	3	3	2	1	5	4	3	13	18.000000	neutral or dissatisfied
2	121447	Male	Loyal Customer	31	Business travel	Eco	290	2	3	3	3	2	2	2	2	4	4	3	4	2	2	0	0.000000	neutral or dissatisfied
3	57040	Male	Loyal Customer	51	Personal Travel	Eco Plus	2565	3	1	3	4	3	5	5	1	1	4	3	5	2	5	38	24.000000	neutral or dissatisfied
4	82822	Female	Loyal Customer	34	Personal Travel	Business	594	1	4	1	3	2	4	5	2	2	1	2	4	2	4	2	0.000000	neutral or dissatisfied
5	41937	Female	Loyal Customer	35	Personal Travel	Eco	129	2	4	0	1	4	0	5	4	4	2	4	4	4	4	0	3.000000	neutral or dissatisfied
6	123931	Female	Loyal Customer	46	Personal Travel	Eco	541	3	4	3	2	3	5	4	1	1	3	1	5	1	4	0	1.000000	neutral or dissatisfied
7	823	Male	Loyal Customer	60	Business travel	Business	102	1	1	5	1	2	5	5	5	5	5	5	4	5	3	0	0.000000	satisfied
8	74179	Male	Loyal Customer	8	Personal Travel	Eco	641	2	5	3	4	4	3	4	4	4	2	4	4	4	4	0	0.000000	neutral or dissatisfied
9	19394	Female	Loyal Customer	64	Personal Travel	Eco Plus	844	3	4	3	3	3	5	4	4	4	3	4	3	4	5	17	10.000000	neutral or dissatisfied
10	13238	Female	Loyal Customer	77	Business travel	Eco	468	2	4	5	5	5	4	4	2	2	2	2	1	2	1	0	0.000000	neutral or dissatisfied
11	3393	Male	disloyal Customer	26	Business travel	Eco	296	2	2	2	3	3	2	3	3	3	1	4	3	3	3	2	0.000000	neutral or dissatisfied
12	28194	Female	Loyal Customer	55	Personal Travel	Eco	859	2	5	2	5	3	4	4	1	1	2	1	3	1	4	0	0.000000	neutral or dissatisfied
13	3864	Male	Loyal Customer	33	Personal Travel	Business	213	4	1	4	5	4	4	4	4	5	3	4	3	4	4	10	15.000000	satisfied
14	94060	Female	Loyal Customer	49	Business travel	Business	323	3	1	1	1	2	2	2	3	3	2	3	4	3	3	2	0.000000	neutral or dissatisfied
15	94228	Female	Loyal Customer	48	Business travel	Business	2984	0	0	0	3	3	5	4	2	2	2	2	5	2	4	0	0.000000	satisfied
16	59340	Female	Loyal Customer	44	Business travel	Business	2399	1	1	1	1	2	5	4	4	4	4	4	5	4	3	19	3.000000	satisfied
17	40535	Male	Loyal Customer	55	Business travel	Eco	411	5	3	3	3	5	5	5	5	4	2	2	1	5	5	0	0.000000	satisfied
18	41844	Female	Loyal Customer	36	Business travel	Eco Plus	462	3	4	4	4	3	5	3	3	1	1	5	4	5	3	0	0.000000	satisfied
19	10833	Male	Loyal Customer	22	Business travel	Business	2027	2	2	2	2	4	4	4	4	5	3	4	4	4	4	7	2.000000	satisfied
20	64382	Male	Loyal Customer	47	Personal Travel	Eco	1158	2	4	2	1	5	2	5	5	2	3	3	4	2	5	0	10.000000	neutral or dissatisfied
21	69930	Male	Loyal Customer	63	Personal Travel	Eco Plus	1514	2	4	1	3	5	1	1	5	3	4	3	2	3	5	0	0.000000	neutral or dissatisfied
22	36623	Female	Loyal Customer	40	Business travel	Business	3044	1	1	4	1	3	3	2	4	4	4	4	5	4	1	1	17.000000	satisfied
23	29148	Female	Loyal Customer	39	Personal Travel	Eco	679	1	4	2	4	4	2	1	4	5	5	4	4	4	4	0	0.000000	neutral or dissatisfied
24	11472	Female	Loyal Customer	39	Business travel	Eco Plus	316	2	1	1	1	2	2	2	2	2	2	3	1	4	2	0	0.000000	neutral or dissatisfied
25	123077	Female	disloyal Customer	38	Business travel	Business	590	5	5	5	3	5	5	5	5	4	4	5	3	5	5	0	0.000000	satisfied
26	1182	Male	Loyal Customer	41	Personal Travel	Eco	247	5	5	5	5	4	5	4	4	3	3	4	3	5	4	0	0.000000	satisfied
27	33175	Male	Loyal Customer	27	Personal Travel	Eco	216	3	0	3	2	4	3	4	4	4	2	3	4	4	4	0	0.000000	neutral or dissatisfied
28	106049	Female	Loyal Customer	12	Personal Travel	Eco	452	3	4	3	5	2	3	2	2	5	4	4	3	4	2	0	0.000000	neutral or dissatisfied
29	76730	Male	Loyal Customer	47	Business travel	Eco Plus	147	3	4	4	4	3	3	3	1	2	3	4	3	3	3	184	176.000000	neutral or dissatisfied
30	129317	Male	Loyal Customer	41	Business travel	Business	2586	2	2	2	2	5	3	4	5	5	5	5	3	5	4	0	3.000000	satisfied
31	61598	Male	Loyal Customer	41	Business travel	Business	1346	3	3	3	3	4	5	5	5	5	5	5	4	5	4	0	0.000000	satisfied
32	19720	Male	Loyal Customer	51	Personal Travel	Eco	475	4	2	4	3	3	4	3	3	2	4	3	1	4	3	3	22.000000	satisfied
33	23209	Male	Loyal Customer	51	Business travel	Business	1594	3	2	2	2	5	3	4	3	3	3	3	2	3	1	0	0.000000	neutral or dissatisfied
34	125641	Female	Loyal Customer	39	Business travel	Business	2984	4	2	4	4	3	5	5	4	4	4	4	3	4	4	5	0.000000	satisfied
35	34268	Female	disloyal Customer	20	Business travel	Eco	280	0	5	0	2	4	0	4	4	2	5	5	4	4	4	0	0.000000	satisfied
36	96334	Male	Loyal Customer	39	Business travel	Business	1609	3	3	3	3	4	5	4	4	4	4	4	5	4	3	47	58.000000	satisfied
37	125007	Female	Loyal Customer	42	Business travel	Eco	184	2	1	1	1	5	2	4	2	2	2	2	3	2	1	0	0.000000	satisfied
38	33069	Male	Loyal Customer	50	Personal Travel	Eco	216	2	0	2	5	1	2	1	1	3	3	5	3	5	1	0	0.000000	neutral or dissatisfied
39	110049	Female	Loyal Customer	55	Business travel	Business	3636	2	2	2	2	3	3	5	5	5	5	5	1	5	4	0	0.000000	satisfied